// Copyright (c) 2025 Huawei Technologies Co., Ltd
// All rights reserved.
//
// Licensed under the BSD 3-Clause License  (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

#include "op_plugin/OpApiInterface.h"
#include "op_plugin/utils/OpAdapter.h"
#include "op_plugin/utils/op_api_common.h"

namespace op_api {
const int FOUR_DIM = 4;
const int AXIS_THREE = 3;
const int AXIS_TWO = 2;
using npu_preparation = at_npu::native::OpPreparation;

namespace {
at::Tensor _linspace_from_neg_one(const at::Tensor& grad, int64_t num_steps, bool align_corners)
{
    if (num_steps <= 1) {
        return at::tensor(0, grad.options());
    }
    auto range = at::linspace(-1, 1, num_steps, grad.options());
    if (!align_corners && num_steps != 0) {
        range = range * (num_steps - 1) / num_steps;
    }
    return range;
}
} // namespace

at::Tensor affine_grid_generator_backward(
    const at::Tensor& grad,
    at::IntArrayRef size,
    bool align_corners)
{
    TORCH_CHECK(size.size() == FOUR_DIM, "AffineGridGeneratorBackward needs 4d (spatial) input."
        + OPS_ERROR(ErrCode::PARAM));
    c10::SmallVector<int64_t, SIZE> output_size = {size[0], 3, 2};
    at::Tensor result = npu_preparation::apply_tensor_with_format(grad, output_size, ACL_FORMAT_ND);
    c10::SmallVector<int64_t, SIZE> assist_size = {size[0], size[2], size[3], 3};
    at::Tensor assist = npu_preparation::apply_tensor_without_format(grad, assist_size);
    assist.select(-1, 0).copy_(_linspace_from_neg_one(grad, size[AXIS_THREE], align_corners));
    assist.select(-1, 1).copy_(_linspace_from_neg_one(grad, size[AXIS_TWO], align_corners).unsqueeze_(-1));
    assist.select(-1, AXIS_TWO).fill_(1);
    AT_ASSERT(grad.sizes() == at::IntArrayRef({size[0], size[2], size[3], 2}), OPS_ERROR(ErrCode::VALUE));

    auto reassist = assist.view({size[0], size[2] * size[3], 3}).transpose(1, 2);
    auto grad_view = grad.view({size[0], size[2] * size[3], 2});
    int8_t cube_math_type = npu_preparation::get_cube_math_type(at_npu::native::env::IsAllowConvHF32());
    EXEC_NPU_CMD(aclnnBatchMatMul, reassist, grad_view, result, cube_math_type);
    auto fresult = result.transpose(1, 2);
    return fresult;
}
} // namespace op_api
